In the rapidly evolving landscape of artificial intelligence (AI), data platforms serve as the backbone that supports the complex demands of both operational and analytic workloads. Intelligent applications powered by generative artificial intelligence (GenAI) and other AI forms are driving the growing need for advanced data platforms. According to a recent report by the Information Services Group (ISG), this demand is expected to surge through 2027. As organizations increasingly look to develop personalized and contextually aware applications, hybrid data platforms capable of handling diverse workloads will become critical.
The Rising Demand for Hybrid Data Platforms
The ISG report underscores an urgent need for advanced data platforms capable of managing real-time analytics and operational data processing simultaneously. This increasing demand is closely tied to the substantial investments being made in AI technologies. Hybrid data platforms are becoming pivotal to the new generation of intelligent applications, which require the seamless integration of operational and analytic processes to deliver both functional and contextual recommendations.
By the end of 2027, the necessity for hybrid platforms is expected to intensify, driven by organizations striving to provide personalized and contextually aware experiences through GenAI. These platforms will need to adeptly balance the dual demands of operational tasks and analytical insights, ensuring that businesses can make real-time decisions based on accurate and comprehensive data. This ability to harmonize operational data processing with real-time analytics will be the cornerstone of delivering functional and contextual recommendations essential for informed decision-making.
Current Adoption Rates and Technological Landscape
Despite the anticipated rise in demand for hybrid data platforms, current adoption rates of GenAI capabilities by data platform providers remain in their infancy. ISG’s evaluation of more than 40 providers reveals that only a quarter have general digital assistant capabilities that can convert natural language questions into database queries. Additionally, ten providers have these capabilities in the preview stage, while the rest are still under development.
The evolutionary landscape of data platforms indicates that organizations are increasingly recognizing the critical role that advanced data platforms play in supporting the deployment of AI-driven applications. This evolving recognition is essential as the ability to process and analyze data in real-time becomes integral for applications requiring precise predictions and recommendations. Both operational and analytic data platforms are thus cemented as indispensable for enabling these real-time functionalities, highlighting their importance in the broader AI ecosystem.
Specialized Data Platforms: Operational vs. Analytic
While the allure of hybrid platforms intensifies, specialized analytic and operational data platforms continue to hold significant roles within the AI ecosystem. Operational data platforms are crucial for running core business functions efficiently, such as finance, operations, sales, and customer experience management. These platforms are tailored to handle the daily operations and transactional processes that are essential for keeping businesses operationally sound.
In contrast, analytic data platforms are designed for decision support, business intelligence, data science, and AI/ML analysis. These platforms enable organizations to extract valuable insights from their data, guiding strategic decision-making processes. Understanding the distinction between these specialized platforms is vital for businesses looking to select the appropriate data platform based on their unique workload requirements. The need for both operational excellence and informed analytical insights underscores the complementary roles these specialized platforms play in the AI landscape.
The Shift to Non-Relational Data Models
ISG’s research illuminates a significant shift from traditional relational data models to non-relational data platforms, such as NoSQL databases and object storage. This transition is largely fueled by the growing importance of AI-driven intelligent operational applications. As these applications become more complex, the boundaries between operational and analytic data platforms begin to blur, underscoring the necessity for flexible and scalable data solutions.
Non-relational data models offer enhanced flexibility and scalability, making them well-suited for the dynamic and voluminous data landscapes that modern AI applications navigate. Their ability to efficiently handle diverse data types and evolving data schemas is becoming increasingly critical as businesses seek to leverage the full potential of AI. This shift represents a paradigm change in how data is managed, reflecting the need for platforms that can adapt to the continuous growth and transformation inherent in AI-driven environments.
Security, Reliability, and Adaptability in Data Platforms
In the realm of advanced data platforms, ensuring security, reliability, and adaptability is paramount. Mark Smith, a partner at ISG Software Research, emphasizes that data platforms are foundational to the digital infrastructure of enterprises and must enable the governed and ethical use of AI. These platforms must be trusted to ensure data integrity, secure to protect sensitive information, and adaptable to seamlessly operate across both cloud and on-premises environments.
Meeting these criteria is essential for enterprises aiming to effectively harness AI technologies. A trusted and secure data platform not only safeguards data but also builds confidence in the AI-driven insights and recommendations derived from that data. Ensuring that these platforms are reliable and adaptable enables enterprises to stay agile in the ever-evolving technological landscape, facilitating continuous innovation and resilience in AI deployments.
Evaluation and Rankings of Leading Data Platform Providers
The ISG 2024 Data Platforms Buyers Guide provides a comprehensive evaluation of 42 software providers across three specific categories: Data Platforms, Operational Data Platforms, and Analytic Data Platforms. The evaluation criteria span seven product and customer experience dimensions, offering a nuanced overview of each provider’s strengths and weaknesses. This guide serves as an invaluable resource for enterprises seeking robust and reliable data platform solutions.
ISG’s evaluations have named Oracle, IBM, and Microsoft as leaders in both the Data Platforms and Operational Data Platforms categories. Meanwhile, Oracle, Teradata, and IBM were recognized as leading providers in the Analytic Data Platforms category. These rankings offer critical insights, guiding enterprises in making informed investment decisions in data platform technologies that are critical to their operational and strategic objectives.
Comprehensive Research and Findings
In the swiftly changing field of artificial intelligence (AI), data platforms are crucial, underpinning the intricate demands of both operational and analytical workloads. Intelligent applications, powered by generative artificial intelligence (GenAI) and various other AI technologies, are fueling an increasing need for sophisticated data platforms. A recent report by the Information Services Group (ISG) highlights that this demand is set to grow significantly up to 2027.
Organizations are progressively keen on developing applications that are both personalized and context-aware. In this quest, hybrid data platforms that can manage a range of workloads are becoming essential. These platforms need to be versatile enough to support the unique requirements of modern AI-driven applications. Whether it’s handling large-scale data analysis or real-time operational tasks, the capability of these platforms is instrumental in enabling the next wave of technological advancements.
As we move forward, the symbiotic relationship between AI and data platforms will only deepen. Organizations will continue to invest heavily in these technologies, aiming to harness the full potential of AI to offer more personalized, efficient, and insightful services. This dynamic evolution underscores the critical role that advanced data platforms play in supporting the cutting-edge applications of tomorrow.
